Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning.
Haiting ChaiQuan GuDavid L RobertsonJoseph HughesPublished in: GigaScience (2022)
ISGs have some unique properties that make them different from the non-ISGs. The representation of some properties has a strong correlation with gene expression following IFN-α stimulation, which can be used as a predictive feature in machine learning. Our model predicts several genes as putative ISGs that so far have shown no significant differential expression when stimulated with IFN-α in the cell/tissue types in the available databases. A web server implementing our method is accessible at http://isgpre.cvr.gla.ac.uk/. The docker image at https://hub.docker.com/r/hchai01/isgpre can be downloaded to reproduce the prediction.
Keyphrases
- machine learning
- big data
- gene expression
- bioinformatics analysis
- dendritic cells
- deep learning
- artificial intelligence
- genome wide
- immune response
- endothelial cells
- dna methylation
- poor prognosis
- single cell
- genome wide identification
- cell therapy
- electronic health record
- induced pluripotent stem cells
- pluripotent stem cells
- stem cells
- small molecule
- quality improvement
- long non coding rna
- binding protein
- neural network
- network analysis